ADHD classification by dual subspace learning using resting-state functional connectivity.
As one of the most common neurobehavioral diseases in school-age children, Attention Deficit Hyperactivity Disorder (ADHD) has been increasingly studied in recent years. But it is still a challenge problem to accurately identify ADHD patients from healthy persons. To address this issue, we propose a...
| Publicado en: | Artificial Intelligence in Medicine Vol. 103 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Mar2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142044762&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142044762 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Mar2020 vid: 103 pid: 1004 pub: Elsevier B.V. artinfo: ui: 142044762 142044762 NLM32143793 142044762 10.1016/j.artmed.2019.101786 NLM32143793 142044762 ppct: 1 formats: tig: atl: ADHD classification by dual subspace learning using resting-state functional connectivity. aug: au: Chen, Ying Tang, Yibin Wang, Chun Liu, Xiaofeng Zhao, Li Wang, Zhishun affil: Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, Southeast University, China sug: subj: Brain Physiopathology Attention Deficit Hyperactivity Disorder Physiopathology Artificial Intelligence Attention Deficit Hyperactivity Disorder Diagnosis Brain Algorithms Human Adolescence Male Female Child Attention Deficit Hyperactivity Disorder Magnetic Resonance Imaging Validation Studies Comparative Studies Evaluation Research Multicenter Studies Clinical Assessment Tools Adolescent: 13-18 years Child: 6-12 years Male Female ab: As one of the most common neurobehavioral diseases in school-age children, Attention Deficit Hyperactivity Disorder (ADHD) has been increasingly studied in recent years. But it is still a challenge problem to accurately identify ADHD patients from healthy persons. To address this issue, we propose a dual subspace classification algorithm by using individual resting-state Functional Connectivity (FC). In detail, two subspaces respectively containing ADHD and healthy control features, called as dual subspaces, are learned with several subspace measures, wherein a modified graph embedding measure is employed to enhance the intra-class relationship of these features. Therefore, given a subject (used as test data) with its FCs, the basic classification principle is to compare its projected component energy of FCs on each subspace and then predict the ADHD or control label according to the subspace with larger energy. However, this principle in practice works with low efficiency, since the dual subspaces are unstably obtained from ADHD databases of small size. Thereby, we present an ADHD classification framework by a binary hypothesis testing of test data. Here, the FCs of test data with its ADHD or control label hypothesis are employed in the discriminative FC selection of training data to promote the stability of dual subspaces. For each hypothesis, the dual subspaces are learned from the selected FCs of training data. The total projected energy of these FCs is also calculated on the subspaces. Sequentially, the energy comparison is carried out under the binary hypotheses. The ADHD or control label is finally predicted for test data with the hypothesis of larger total energy. In the experiments on ADHD-200 dataset, our method achieves a significant classification performance compared with several state-of-the-art machine learning and deep learning methods, where our accuracy is about 90 % for most of ADHD databases in the leave-one-out cross-validation test. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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